urban-exposure-review-radar-workflow

urban-exposure-review-radar-workflow is a skill for Codex from Lzy599775/agent-auto-sci-skills. It costs 171 tokens per session (2,878 once invoked), scanned A, original, MIT.

A Chinese-language workflow for research reviews and frontier scanning across geography, sports, urban health and remote sensing. Remote sensing means studying places using data collected by satellites or other sensors.

In plain words
What is it for?
Use it for work on green or heat exposure, sports facilities, parks, accessibility, spatial fairness, urban health, satellite data, systematic reviews and bibliometric reviews.
Why use it?
It helps turn a broad idea into a reproducible review or research plan with search, screening, coding, analysis and citation checks.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions Codex.

Good fit Use it for work on green or heat exposure, sports facilities, parks, accessibility, spatial fairness, urban health, satellite data, systematic reviews and bibliometric reviews.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lzy599775/agent-auto-sci-skills/urban-exposure-review-radar-workflow
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Lzy599775/agent-auto-sci-skills --skill urban-exposure-review-radar-workflow
Clone the repo
git clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for urban-exposure-review-radar-workflow

README.md
[![agentmods](https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/urban-exposure-review-radar-workflow/github.svg)](https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/urban-exposure-review-radar-workflow)
Your own site
<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/urban-exposure-review-radar-workflow"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/urban-exposure-review-radar-workflow/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for urban-exposure-review-radar-workflow

Your own site · 80×15
<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/urban-exposure-review-radar-workflow"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/urban-exposure-review-radar-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 171 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,878 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00171 $0.02878
Opus 5 $0.00086 $0.01439
Sonnet 5 $0.00034 $0.00576
Haiku 4.5 $0.00017 $0.00288

Measured 12d ago against content hash e5dd72823c82, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

urban-exposure-review-radar-workflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/create_review_radar_scaffold.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/urban-exposure-review-radar-workflow/SKILL.md · 184 lines

How it starts

The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Urban Exposure Review Radar Workflow

This skill turns a broad geography + sport + urban health idea into a reproducible review, frontier radar, or review-informed empirical research plan.

It combines two transferable patterns:

  • review workflow discipline: project structure, search, screening, extraction, synthesis, drafting, audit, citation export;
  • research radar discipline: current-source scanning, remote-sensing / CV transfer analysis, ranked reading queues, publishable idea generation.

Output Contract

For every substantial request, return these blocks unless the user asks for a smaller answer:

  1. 研究定位: review type, target journal route, contribution boundary.
  2. 流程路线: staged workflow with inputs, outputs, and checkpoints.
  3. 检索/雷达策略: formal databases and, when needed, frontier sources.
  4. 提取与编码: variables, coding fields, quality or bias checks.
  5. 分析与图表: bibliometric, evidence-map, spatial, remote-sensing, or health-linkage outputs.
  6. 写作与门控: manuscript structure, claim-evidence audit, citation and genre checks.
  7. 风险与下一步: failure modes, fallback, and the next concrete action.

If the user asks for an executable setup, create or recommend a project scaffold using scripts/create_review_radar_scaffold.py.

Workflow

  1. Classify the task

    • Choose exactly one primary route: narrative review, systematic review, scoping review, bibliometric + critical review, frontier radar, empirical study design, or hybrid.
    • If the route is ambiguous, stop at a checkpoint and ask the user to choose.
    • Read references/workflow_playbook.md.
  2. Set the journal and contribution boundary

    • SCS / Cities: emphasize urban governance, planning mechanism, spatial equity, sustainability, policy agenda.
    • Health & Place / Environment International / Environmental Research: emphasize exposure validity, health outcome definition, confounding, causal caution.
    • Urban Forestry & Urban Greening / Landscape and Urban Planning: emphasize green infrastructure, landscape planning, ecosystem and social benefits.
    • Nature-family urban/health outlets: require strong conceptual novelty, data credibility, mechanism, and generalizable contribution.

Read the full file on GitHub · 184 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 184 lines · 171 tokens per session scan A e5dd72823c82

Subscribe to this mod's changes

urban-exposure-review-radar-workflow is a skill published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 6d ago), licensed MIT. It adds 171 tokens to every session and 2,878 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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